Overview

Dataset statistics

Number of variables27
Number of observations132241
Missing cells615435
Missing cells (%)17.2%
Duplicate rows5864
Duplicate rows (%)4.4%
Total size in memory32.3 MiB
Average record size in memory256.0 B

Variable types

Categorical16
Numeric11

Alerts

Dataset has 5864 (4.4%) duplicate rowsDuplicates
EXTENSION_COUNT is highly imbalanced (55.6%)Imbalance
WIND_TURBINE_COUNT is highly imbalanced (99.2%)Imbalance
SOLAR_WATER_HEATING_FLAG is highly imbalanced (97.7%)Imbalance
BUILT_FORM has 5746 (4.3%) missing valuesMissing
MAINS_GAS_FLAG has 18965 (14.3%) missing valuesMissing
FLAT_TOP_STOREY has 60385 (45.7%) missing valuesMissing
FLAT_STOREY_COUNT has 118657 (89.7%) missing valuesMissing
MULTI_GLAZE_PROPORTION has 13523 (10.2%) missing valuesMissing
EXTENSION_COUNT has 18714 (14.2%) missing valuesMissing
NUMBER_HABITABLE_ROOMS has 18714 (14.2%) missing valuesMissing
NUMBER_HEATED_ROOMS has 18714 (14.2%) missing valuesMissing
LOW_ENERGY_LIGHTING has 6277 (4.7%) missing valuesMissing
NUMBER_OPEN_FIREPLACES has 3055 (2.3%) missing valuesMissing
WIND_TURBINE_COUNT has 9729 (7.4%) missing valuesMissing
FLOOR_HEIGHT has 63153 (47.8%) missing valuesMissing
PHOTO_SUPPLY has 53380 (40.4%) missing valuesMissing
SOLAR_WATER_HEATING_FLAG has 44350 (33.5%) missing valuesMissing
CONSTRUCTION_AGE_BAND has 14876 (11.2%) missing valuesMissing
FIXED_LIGHTING_OUTLETS_COUNT has 57395 (43.4%) missing valuesMissing
LOW_ENERGY_FIXED_LIGHT_COUNT has 89802 (67.9%) missing valuesMissing
NUMBER_OPEN_FIREPLACES is highly skewed (γ1 = 67.18573375)Skewed
FLOOR_HEIGHT is highly skewed (γ1 = 41.26470328)Skewed
PHOTO_SUPPLY is highly skewed (γ1 = 29.64736707)Skewed
LOW_ENERGY_FIXED_LIGHT_COUNT is highly skewed (γ1 = 21.2043161)Skewed
MULTI_GLAZE_PROPORTION has 9411 (7.1%) zerosZeros
LOW_ENERGY_LIGHTING has 19391 (14.7%) zerosZeros
NUMBER_OPEN_FIREPLACES has 120047 (90.8%) zerosZeros
PHOTO_SUPPLY has 78723 (59.5%) zerosZeros
LOW_ENERGY_FIXED_LIGHT_COUNT has 6491 (4.9%) zerosZeros

Reproduction

Analysis started2024-02-25 23:28:06.665648
Analysis finished2024-02-25 23:28:42.433397
Duration35.77 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
D
49010 
C
41672 
E
18568 
B
18348 
F
 
3513
Other values (2)
 
1130

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132241
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowD
3rd rowC
4th rowC
5th rowE

Common Values

ValueCountFrequency (%)
D 49010
37.1%
C 41672
31.5%
E 18568
 
14.0%
B 18348
 
13.9%
F 3513
 
2.7%
G 770
 
0.6%
A 360
 
0.3%

Length

2024-02-25T23:28:42.625784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:42.816331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
d 49010
37.1%
c 41672
31.5%
e 18568
 
14.0%
b 18348
 
13.9%
f 3513
 
2.7%
g 770
 
0.6%
a 360
 
0.3%

Most occurring characters

ValueCountFrequency (%)
D 49010
37.1%
C 41672
31.5%
E 18568
 
14.0%
B 18348
 
13.9%
F 3513
 
2.7%
G 770
 
0.6%
A 360
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 132241
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 49010
37.1%
C 41672
31.5%
E 18568
 
14.0%
B 18348
 
13.9%
F 3513
 
2.7%
G 770
 
0.6%
A 360
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 132241
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 49010
37.1%
C 41672
31.5%
E 18568
 
14.0%
B 18348
 
13.9%
F 3513
 
2.7%
G 770
 
0.6%
A 360
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 49010
37.1%
C 41672
31.5%
E 18568
 
14.0%
B 18348
 
13.9%
F 3513
 
2.7%
G 770
 
0.6%
A 360
 
0.3%

PROPERTY_TYPE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Flat
75406 
House
48553 
Maisonette
 
6258
Bungalow
 
1995
Park home
 
29

Length

Max length10
Median length4
Mean length4.7125324
Min length4

Characters and Unicode

Total characters623190
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouse
2nd rowHouse
3rd rowFlat
4th rowFlat
5th rowFlat

Common Values

ValueCountFrequency (%)
Flat 75406
57.0%
House 48553
36.7%
Maisonette 6258
 
4.7%
Bungalow 1995
 
1.5%
Park home 29
 
< 0.1%

Length

2024-02-25T23:28:43.015784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:43.176445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
flat 75406
57.0%
house 48553
36.7%
maisonette 6258
 
4.7%
bungalow 1995
 
1.5%
park 29
 
< 0.1%
home 29
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
t 87922
14.1%
a 83688
13.4%
l 77401
12.4%
F 75406
12.1%
e 61098
9.8%
o 56835
9.1%
s 54811
8.8%
u 50548
8.1%
H 48553
7.8%
n 8253
 
1.3%
Other values (11) 18675
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 490920
78.8%
Uppercase Letter 132241
 
21.2%
Space Separator 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 87922
17.9%
a 83688
17.0%
l 77401
15.8%
e 61098
12.4%
o 56835
11.6%
s 54811
11.2%
u 50548
10.3%
n 8253
 
1.7%
i 6258
 
1.3%
g 1995
 
0.4%
Other values (5) 2111
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
F 75406
57.0%
H 48553
36.7%
M 6258
 
4.7%
B 1995
 
1.5%
P 29
 
< 0.1%
Space Separator
ValueCountFrequency (%)
29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 623161
> 99.9%
Common 29
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 87922
14.1%
a 83688
13.4%
l 77401
12.4%
F 75406
12.1%
e 61098
9.8%
o 56835
9.1%
s 54811
8.8%
u 50548
8.1%
H 48553
7.8%
n 8253
 
1.3%
Other values (10) 18646
 
3.0%
Common
ValueCountFrequency (%)
29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 623190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 87922
14.1%
a 83688
13.4%
l 77401
12.4%
F 75406
12.1%
e 61098
9.8%
o 56835
9.1%
s 54811
8.8%
u 50548
8.1%
H 48553
7.8%
n 8253
 
1.3%
Other values (11) 18675
 
3.0%

BUILT_FORM
Categorical

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing5746
Missing (%)4.3%
Memory size6.0 MiB
Semi-Detached
42022 
Mid-Terrace
35832 
Detached
22143 
End-Terrace
20151 
Enclosed End-Terrace
 
3655

Length

Max length20
Median length13
Mean length11.590838
Min length8

Characters and Unicode

Total characters1466183
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSemi-Detached
2nd rowEnd-Terrace
3rd rowMid-Terrace
4th rowSemi-Detached
5th rowSemi-Detached

Common Values

ValueCountFrequency (%)
Semi-Detached 42022
31.8%
Mid-Terrace 35832
27.1%
Detached 22143
16.7%
End-Terrace 20151
15.2%
Enclosed End-Terrace 3655
 
2.8%
Enclosed Mid-Terrace 2692
 
2.0%
(Missing) 5746
 
4.3%

Length

2024-02-25T23:28:43.356199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:43.525003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
semi-detached 42022
31.6%
mid-terrace 38524
29.0%
end-terrace 23806
17.9%
detached 22143
16.7%
enclosed 6347
 
4.8%

Most occurring characters

ValueCountFrequency (%)
e 301359
20.6%
d 132842
9.1%
c 132842
9.1%
a 126495
8.6%
r 124660
8.5%
- 104352
 
7.1%
i 80546
 
5.5%
D 64165
 
4.4%
t 64165
 
4.4%
h 64165
 
4.4%
Other values (10) 270592
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1118290
76.3%
Uppercase Letter 237194
 
16.2%
Dash Punctuation 104352
 
7.1%
Space Separator 6347
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 301359
26.9%
d 132842
11.9%
c 132842
11.9%
a 126495
11.3%
r 124660
11.1%
i 80546
 
7.2%
t 64165
 
5.7%
h 64165
 
5.7%
m 42022
 
3.8%
n 30153
 
2.7%
Other values (3) 19041
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
D 64165
27.1%
T 62330
26.3%
S 42022
17.7%
M 38524
16.2%
E 30153
12.7%
Dash Punctuation
ValueCountFrequency (%)
- 104352
100.0%
Space Separator
ValueCountFrequency (%)
6347
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1355484
92.4%
Common 110699
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 301359
22.2%
d 132842
9.8%
c 132842
9.8%
a 126495
9.3%
r 124660
9.2%
i 80546
 
5.9%
D 64165
 
4.7%
t 64165
 
4.7%
h 64165
 
4.7%
T 62330
 
4.6%
Other values (8) 201915
14.9%
Common
ValueCountFrequency (%)
- 104352
94.3%
6347
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1466183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 301359
20.6%
d 132842
9.1%
c 132842
9.1%
a 126495
8.6%
r 124660
8.5%
- 104352
 
7.1%
i 80546
 
5.5%
D 64165
 
4.4%
t 64165
 
4.4%
h 64165
 
4.4%
Other values (10) 270592
18.5%

TOTAL_FLOOR_AREA
Real number (ℝ)

Distinct12020
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.44702
Minimum0
Maximum3438
Zeros305
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:43.732633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q152.25
median71
Q398
95-th percentile180
Maximum3438
Range3438
Interquartile range (IQR)45.75

Descriptive statistics

Standard deviation63.545944
Coefficient of variation (CV)0.7524948
Kurtosis181.03443
Mean84.44702
Median Absolute Deviation (MAD)21
Skewness8.0384356
Sum11167358
Variance4038.0869
MonotonicityNot monotonic
2024-02-25T23:28:43.949038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 1693
 
1.3%
72 1627
 
1.2%
64 1582
 
1.2%
60 1572
 
1.2%
70 1571
 
1.2%
51 1527
 
1.2%
65 1496
 
1.1%
63 1489
 
1.1%
66 1479
 
1.1%
61 1478
 
1.1%
Other values (12010) 116727
88.3%
ValueCountFrequency (%)
0 305
0.2%
0.1 2
 
< 0.1%
0.88 1
 
< 0.1%
1 1
 
< 0.1%
2.12 1
 
< 0.1%
2.3 1
 
< 0.1%
3 1
 
< 0.1%
3.2 1
 
< 0.1%
3.87 1
 
< 0.1%
4.17 1
 
< 0.1%
ValueCountFrequency (%)
3438 1
< 0.1%
2363 1
< 0.1%
2356 1
< 0.1%
2350.32 1
< 0.1%
1861.72 1
< 0.1%
1841 1
< 0.1%
1829 1
< 0.1%
1801 1
< 0.1%
1769 1
< 0.1%
1672 1
< 0.1%

MAINS_GAS_FLAG
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing18965
Missing (%)14.3%
Memory size6.0 MiB
1.0
97000 
0.0
16276 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters339828
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 97000
73.4%
0.0 16276
 
12.3%
(Missing) 18965
 
14.3%

Length

2024-02-25T23:28:44.141175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:44.274753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 97000
85.6%
0.0 16276
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 129552
38.1%
. 113276
33.3%
1 97000
28.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 226552
66.7%
Other Punctuation 113276
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 129552
57.2%
1 97000
42.8%
Other Punctuation
ValueCountFrequency (%)
. 113276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 339828
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 129552
38.1%
. 113276
33.3%
1 97000
28.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 339828
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 129552
38.1%
. 113276
33.3%
1 97000
28.5%

FLAT_TOP_STOREY
Categorical

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing60385
Missing (%)45.7%
Memory size6.0 MiB
0.0
44109 
1.0
27747 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters215568
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 44109
33.4%
1.0 27747
21.0%
(Missing) 60385
45.7%

Length

2024-02-25T23:28:44.440232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:44.605544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 44109
61.4%
1.0 27747
38.6%

Most occurring characters

ValueCountFrequency (%)
0 115965
53.8%
. 71856
33.3%
1 27747
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 143712
66.7%
Other Punctuation 71856
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115965
80.7%
1 27747
 
19.3%
Other Punctuation
ValueCountFrequency (%)
. 71856
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 215568
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 115965
53.8%
. 71856
33.3%
1 27747
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 215568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 115965
53.8%
. 71856
33.3%
1 27747
 
12.9%

FLAT_STOREY_COUNT
Real number (ℝ)

MISSING 

Distinct22
Distinct (%)0.2%
Missing118657
Missing (%)89.7%
Infinite0
Infinite (%)0.0%
Mean3.2533127
Minimum0
Maximum33
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:44.770707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8183614
Coefficient of variation (CV)0.55892611
Kurtosis29.566848
Mean3.2533127
Median Absolute Deviation (MAD)1
Skewness4.3666021
Sum44193
Variance3.3064383
MonotonicityNot monotonic
2024-02-25T23:28:44.951240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 5972
 
4.5%
2 4079
 
3.1%
4 2090
 
1.6%
5 557
 
0.4%
6 255
 
0.2%
1 122
 
0.1%
7 102
 
0.1%
8 98
 
0.1%
11 80
 
0.1%
10 51
 
< 0.1%
Other values (12) 178
 
0.1%
(Missing) 118657
89.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 122
 
0.1%
2 4079
3.1%
3 5972
4.5%
4 2090
 
1.6%
5 557
 
0.4%
6 255
 
0.2%
7 102
 
0.1%
8 98
 
0.1%
9 42
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
31 1
 
< 0.1%
19 1
 
< 0.1%
18 10
 
< 0.1%
17 14
 
< 0.1%
16 30
< 0.1%
15 39
< 0.1%
14 14
 
< 0.1%
13 6
 
< 0.1%
12 18
< 0.1%

MULTI_GLAZE_PROPORTION
Real number (ℝ)

MISSING  ZEROS 

Distinct101
Distinct (%)0.1%
Missing13523
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean87.491341
Minimum0
Maximum100
Zeros9411
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:45.170756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.018473
Coefficient of variation (CV)0.34310221
Kurtosis3.5692313
Mean87.491341
Median Absolute Deviation (MAD)0
Skewness-2.2832524
Sum10386797
Variance901.10871
MonotonicityNot monotonic
2024-02-25T23:28:45.388635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 94416
71.4%
0 9411
 
7.1%
90 1963
 
1.5%
95 1609
 
1.2%
50 1395
 
1.1%
80 1143
 
0.9%
60 693
 
0.5%
85 677
 
0.5%
40 627
 
0.5%
75 607
 
0.5%
Other values (91) 6177
 
4.7%
(Missing) 13523
 
10.2%
ValueCountFrequency (%)
0 9411
7.1%
1 7
 
< 0.1%
2 5
 
< 0.1%
3 7
 
< 0.1%
4 4
 
< 0.1%
5 174
 
0.1%
6 16
 
< 0.1%
7 10
 
< 0.1%
8 20
 
< 0.1%
9 13
 
< 0.1%
ValueCountFrequency (%)
100 94416
71.4%
99 53
 
< 0.1%
98 187
 
0.1%
97 73
 
0.1%
96 83
 
0.1%
95 1609
 
1.2%
94 61
 
< 0.1%
93 49
 
< 0.1%
92 121
 
0.1%
91 57
 
< 0.1%

EXTENSION_COUNT
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing18714
Missing (%)14.2%
Memory size6.0 MiB
0.0
85954 
1.0
21346 
2.0
 
5131
3.0
 
873
4.0
 
223

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters340581
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 85954
65.0%
1.0 21346
 
16.1%
2.0 5131
 
3.9%
3.0 873
 
0.7%
4.0 223
 
0.2%
(Missing) 18714
 
14.2%

Length

2024-02-25T23:28:45.582272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:45.735779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 85954
75.7%
1.0 21346
 
18.8%
2.0 5131
 
4.5%
3.0 873
 
0.8%
4.0 223
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 199481
58.6%
. 113527
33.3%
1 21346
 
6.3%
2 5131
 
1.5%
3 873
 
0.3%
4 223
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 227054
66.7%
Other Punctuation 113527
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 199481
87.9%
1 21346
 
9.4%
2 5131
 
2.3%
3 873
 
0.4%
4 223
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 113527
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 340581
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 199481
58.6%
. 113527
33.3%
1 21346
 
6.3%
2 5131
 
1.5%
3 873
 
0.3%
4 223
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340581
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 199481
58.6%
. 113527
33.3%
1 21346
 
6.3%
2 5131
 
1.5%
3 873
 
0.3%
4 223
 
0.1%

NUMBER_HABITABLE_ROOMS
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)< 0.1%
Missing18714
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean3.8495776
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:45.906923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q35
95-th percentile7
Maximum61
Range60
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9323382
Coefficient of variation (CV)0.50196109
Kurtosis16.917343
Mean3.8495776
Median Absolute Deviation (MAD)1
Skewness1.7765379
Sum437031
Variance3.7339309
MonotonicityNot monotonic
2024-02-25T23:28:46.109281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3 34767
26.3%
4 19001
14.4%
2 17939
13.6%
5 15736
11.9%
6 8490
 
6.4%
1 7186
 
5.4%
7 5023
 
3.8%
8 2674
 
2.0%
9 1367
 
1.0%
10 694
 
0.5%
Other values (20) 650
 
0.5%
(Missing) 18714
14.2%
ValueCountFrequency (%)
1 7186
 
5.4%
2 17939
13.6%
3 34767
26.3%
4 19001
14.4%
5 15736
11.9%
6 8490
 
6.4%
7 5023
 
3.8%
8 2674
 
2.0%
9 1367
 
1.0%
10 694
 
0.5%
ValueCountFrequency (%)
61 1
 
< 0.1%
54 1
 
< 0.1%
43 1
 
< 0.1%
40 1
 
< 0.1%
30 1
 
< 0.1%
25 2
< 0.1%
24 2
< 0.1%
23 3
< 0.1%
22 2
< 0.1%
21 3
< 0.1%

NUMBER_HEATED_ROOMS
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)< 0.1%
Missing18714
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean3.8157795
Minimum0
Maximum43
Zeros402
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:46.304916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q35
95-th percentile7
Maximum43
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9222398
Coefficient of variation (CV)0.5037607
Kurtosis5.125261
Mean3.8157795
Median Absolute Deviation (MAD)1
Skewness1.3250627
Sum433194
Variance3.6950057
MonotonicityNot monotonic
2024-02-25T23:28:46.484803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
3 34650
26.2%
4 18919
14.3%
2 18056
13.7%
5 15452
11.7%
6 8402
 
6.4%
1 7426
 
5.6%
7 4921
 
3.7%
8 2647
 
2.0%
9 1342
 
1.0%
10 680
 
0.5%
Other values (18) 1032
 
0.8%
(Missing) 18714
14.2%
ValueCountFrequency (%)
0 402
 
0.3%
1 7426
 
5.6%
2 18056
13.7%
3 34650
26.2%
4 18919
14.3%
5 15452
11.7%
6 8402
 
6.4%
7 4921
 
3.7%
8 2647
 
2.0%
9 1342
 
1.0%
ValueCountFrequency (%)
43 1
 
< 0.1%
30 1
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
23 1
 
< 0.1%
22 2
 
< 0.1%
21 3
< 0.1%
20 4
< 0.1%
19 4
< 0.1%
18 5
< 0.1%

LOW_ENERGY_LIGHTING
Real number (ℝ)

MISSING  ZEROS 

Distinct106
Distinct (%)0.1%
Missing6277
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean59.540488
Minimum0
Maximum145
Zeros19391
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:46.687017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median68
Q3100
95-th percentile100
Maximum145
Range145
Interquartile range (IQR)80

Descriptive statistics

Standard deviation39.444433
Coefficient of variation (CV)0.66248086
Kurtosis-1.4954277
Mean59.540488
Median Absolute Deviation (MAD)32
Skewness-0.34016127
Sum7499958
Variance1555.8633
MonotonicityNot monotonic
2024-02-25T23:28:46.931798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 46624
35.3%
0 19391
14.7%
50 5097
 
3.9%
80 2345
 
1.8%
67 2299
 
1.7%
20 2270
 
1.7%
33 2209
 
1.7%
75 2173
 
1.6%
25 2073
 
1.6%
60 2029
 
1.5%
Other values (96) 39454
29.8%
(Missing) 6277
 
4.7%
ValueCountFrequency (%)
0 19391
14.7%
1 256
 
0.2%
2 187
 
0.1%
3 439
 
0.3%
4 560
 
0.4%
5 618
 
0.5%
6 641
 
0.5%
7 586
 
0.4%
8 757
 
0.6%
9 465
 
0.4%
ValueCountFrequency (%)
145 1
 
< 0.1%
142 1
 
< 0.1%
107 1
 
< 0.1%
103 1
 
< 0.1%
101 4
 
< 0.1%
100 46624
35.3%
99 9
 
< 0.1%
98 34
 
< 0.1%
97 68
 
0.1%
96 88
 
0.1%

NUMBER_OPEN_FIREPLACES
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing3055
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean0.097309306
Minimum0
Maximum100
Zeros120047
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:47.139471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49520458
Coefficient of variation (CV)5.0889746
Kurtosis12850.273
Mean0.097309306
Median Absolute Deviation (MAD)0
Skewness67.185734
Sum12571
Variance0.24522758
MonotonicityNot monotonic
2024-02-25T23:28:47.336433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 120047
90.8%
1 6899
 
5.2%
2 1606
 
1.2%
3 350
 
0.3%
4 186
 
0.1%
5 55
 
< 0.1%
6 22
 
< 0.1%
7 8
 
< 0.1%
8 8
 
< 0.1%
9 2
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 3055
 
2.3%
ValueCountFrequency (%)
0 120047
90.8%
1 6899
 
5.2%
2 1606
 
1.2%
3 350
 
0.3%
4 186
 
0.1%
5 55
 
< 0.1%
6 22
 
< 0.1%
7 8
 
< 0.1%
8 8
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
< 0.1%
8 8
 
< 0.1%
7 8
 
< 0.1%
6 22
 
< 0.1%
5 55
 
< 0.1%
4 186
0.1%
3 350
0.3%

WIND_TURBINE_COUNT
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing9729
Missing (%)7.4%
Memory size6.0 MiB
0.0
122395 
1.0
 
63
-1.0
 
54

Length

Max length4
Median length3
Mean length3.0004408
Min length3

Characters and Unicode

Total characters367590
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 122395
92.6%
1.0 63
 
< 0.1%
-1.0 54
 
< 0.1%
(Missing) 9729
 
7.4%

Length

2024-02-25T23:28:47.543901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:47.715709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 122395
99.9%
1.0 117
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 244907
66.6%
. 122512
33.3%
1 117
 
< 0.1%
- 54
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 245024
66.7%
Other Punctuation 122512
33.3%
Dash Punctuation 54
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 244907
> 99.9%
1 117
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 122512
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 367590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 244907
66.6%
. 122512
33.3%
1 117
 
< 0.1%
- 54
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 367590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 244907
66.6%
. 122512
33.3%
1 117
 
< 0.1%
- 54
 
< 0.1%

FLOOR_HEIGHT
Real number (ℝ)

MISSING  SKEWED 

Distinct668
Distinct (%)1.0%
Missing63153
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean2.4876789
Minimum0
Maximum37.72
Zeros29
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:47.908349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.28
Q12.38
median2.44
Q32.58
95-th percentile2.82
Maximum37.72
Range37.72
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.35592388
Coefficient of variation (CV)0.14307468
Kurtosis2972.8762
Mean2.4876789
Median Absolute Deviation (MAD)0.09
Skewness41.264703
Sum171868.76
Variance0.12668181
MonotonicityNot monotonic
2024-02-25T23:28:48.155053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4 8562
 
6.5%
2.5 6302
 
4.8%
2.3 3994
 
3.0%
2.6 3009
 
2.3%
2.41 2793
 
2.1%
2.7 1927
 
1.5%
2.45 1804
 
1.4%
2.35 1583
 
1.2%
2.42 1557
 
1.2%
2.43 1513
 
1.1%
Other values (658) 36044
27.3%
(Missing) 63153
47.8%
ValueCountFrequency (%)
0 29
 
< 0.1%
0.1 2
 
< 0.1%
0.23 1
 
< 0.1%
1 202
0.2%
1.5 29
 
< 0.1%
1.51 1
 
< 0.1%
1.58 1
 
< 0.1%
1.59 1
 
< 0.1%
1.6 3
 
< 0.1%
1.63 1
 
< 0.1%
ValueCountFrequency (%)
37.72 1
< 0.1%
28.35 1
< 0.1%
25 1
< 0.1%
24.18 1
< 0.1%
24 1
< 0.1%
23.7 1
< 0.1%
21.91 1
< 0.1%
19.95 1
< 0.1%
19.42 1
< 0.1%
14.81 1
< 0.1%

PHOTO_SUPPLY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing53380
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean0.062857433
Minimum0
Maximum80
Zeros78723
Zeros (%)59.5%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:48.361201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6343984
Coefficient of variation (CV)26.001673
Kurtosis975.91442
Mean0.062857433
Median Absolute Deviation (MAD)0
Skewness29.647367
Sum4957
Variance2.6712581
MonotonicityNot monotonic
2024-02-25T23:28:48.566491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 78723
59.5%
40 25
 
< 0.1%
20 22
 
< 0.1%
50 20
 
< 0.1%
35 15
 
< 0.1%
25 13
 
< 0.1%
30 9
 
< 0.1%
70 7
 
< 0.1%
33 5
 
< 0.1%
45 5
 
< 0.1%
Other values (9) 17
 
< 0.1%
(Missing) 53380
40.4%
ValueCountFrequency (%)
0 78723
59.5%
5 1
 
< 0.1%
9 2
 
< 0.1%
10 3
 
< 0.1%
15 4
 
< 0.1%
20 22
 
< 0.1%
25 13
 
< 0.1%
26 1
 
< 0.1%
28 1
 
< 0.1%
30 9
 
< 0.1%
ValueCountFrequency (%)
80 1
 
< 0.1%
75 2
 
< 0.1%
70 7
 
< 0.1%
60 2
 
< 0.1%
50 20
< 0.1%
45 5
 
< 0.1%
40 25
< 0.1%
35 15
< 0.1%
33 5
 
< 0.1%
30 9
 
< 0.1%

SOLAR_WATER_HEATING_FLAG
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing44350
Missing (%)33.5%
Memory size6.0 MiB
0.0
87695 
1.0
 
196

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters263673
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 87695
66.3%
1.0 196
 
0.1%
(Missing) 44350
33.5%

Length

2024-02-25T23:28:48.769308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:48.930091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 87695
99.8%
1.0 196
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 175586
66.6%
. 87891
33.3%
1 196
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 175782
66.7%
Other Punctuation 87891
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 175586
99.9%
1 196
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 87891
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 263673
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 175586
66.6%
. 87891
33.3%
1 196
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 263673
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 175586
66.6%
. 87891
33.3%
1 196
 
0.1%

CONSTRUCTION_AGE_BAND
Categorical

MISSING 

Distinct13
Distinct (%)< 0.1%
Missing14876
Missing (%)11.2%
Memory size6.0 MiB
England and Wales: 1930-1949
34178 
England and Wales: 1900-1929
24140 
England and Wales: 1950-1966
12746 
England and Wales: 1967-1975
9472 
England and Wales: 1983-1990
7214 
Other values (8)
29615 

Length

Max length31
Median length28
Mean length28.271657
Min length28

Characters and Unicode

Total characters3318103
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngland and Wales: 1900-1929
2nd rowEngland and Wales: 1900-1929
3rd rowEngland and Wales: 1900-1929
4th rowEngland and Wales: 2003-2006
5th rowEngland and Wales: 1930-1949

Common Values

ValueCountFrequency (%)
England and Wales: 1930-1949 34178
25.8%
England and Wales: 1900-1929 24140
18.3%
England and Wales: 1950-1966 12746
 
9.6%
England and Wales: 1967-1975 9472
 
7.2%
England and Wales: 1983-1990 7214
 
5.5%
England and Wales: 2012 onwards 5506
 
4.2%
England and Wales: 1976-1982 4571
 
3.5%
England and Wales: before 1900 4540
 
3.4%
England and Wales: 1996-2002 4166
 
3.2%
England and Wales: 2003-2006 3812
 
2.9%
Other values (3) 7020
 
5.3%
(Missing) 14876
11.2%

Length

2024-02-25T23:28:49.123749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
england 117365
24.4%
and 117365
24.4%
wales 117365
24.4%
1930-1949 34178
 
7.1%
1900-1929 24140
 
5.0%
1950-1966 12746
 
2.6%
1967-1975 9472
 
2.0%
onwards 7601
 
1.6%
1983-1990 7214
 
1.5%
2012 5506
 
1.1%
Other values (8) 28649
 
5.9%

Most occurring characters

ValueCountFrequency (%)
364236
11.0%
a 359696
10.8%
n 359696
10.8%
9 278198
 
8.4%
d 242331
 
7.3%
l 234730
 
7.1%
1 212492
 
6.4%
0 148185
 
4.5%
e 126445
 
3.8%
s 124966
 
3.8%
Other values (17) 867128
26.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1606192
48.4%
Decimal Number 890356
26.8%
Space Separator 364236
 
11.0%
Uppercase Letter 234730
 
7.1%
Other Punctuation 117365
 
3.5%
Dash Punctuation 105224
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 359696
22.4%
n 359696
22.4%
d 242331
15.1%
l 234730
14.6%
e 126445
 
7.9%
s 124966
 
7.8%
g 117365
 
7.3%
o 12141
 
0.8%
r 12141
 
0.8%
w 7601
 
0.5%
Other values (2) 9080
 
0.6%
Decimal Number
ValueCountFrequency (%)
9 278198
31.2%
1 212492
23.9%
0 148185
16.6%
2 60048
 
6.7%
6 47513
 
5.3%
3 45204
 
5.1%
4 34178
 
3.8%
7 26747
 
3.0%
5 26006
 
2.9%
8 11785
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
E 117365
50.0%
W 117365
50.0%
Space Separator
ValueCountFrequency (%)
364236
100.0%
Other Punctuation
ValueCountFrequency (%)
: 117365
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 105224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1840922
55.5%
Common 1477181
44.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 359696
19.5%
n 359696
19.5%
d 242331
13.2%
l 234730
12.8%
e 126445
 
6.9%
s 124966
 
6.8%
E 117365
 
6.4%
W 117365
 
6.4%
g 117365
 
6.4%
o 12141
 
0.7%
Other values (4) 28822
 
1.6%
Common
ValueCountFrequency (%)
364236
24.7%
9 278198
18.8%
1 212492
14.4%
0 148185
10.0%
: 117365
 
7.9%
- 105224
 
7.1%
2 60048
 
4.1%
6 47513
 
3.2%
3 45204
 
3.1%
4 34178
 
2.3%
Other values (3) 64538
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3318103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
364236
11.0%
a 359696
10.8%
n 359696
10.8%
9 278198
 
8.4%
d 242331
 
7.3%
l 234730
 
7.1%
1 212492
 
6.4%
0 148185
 
4.5%
e 126445
 
3.8%
s 124966
 
3.8%
Other values (17) 867128
26.1%

FIXED_LIGHTING_OUTLETS_COUNT
Real number (ℝ)

MISSING 

Distinct158
Distinct (%)0.2%
Missing57395
Missing (%)43.4%
Infinite0
Infinite (%)0.0%
Mean11.9839
Minimum0
Maximum965
Zeros747
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:49.341635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median9
Q313
95-th percentile30
Maximum965
Range965
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.596159
Coefficient of variation (CV)1.0510901
Kurtosis666.92946
Mean11.9839
Median Absolute Deviation (MAD)3
Skewness14.001332
Sum896947
Variance158.66323
MonotonicityNot monotonic
2024-02-25T23:28:49.578468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 9151
 
6.9%
8 7315
 
5.5%
6 7171
 
5.4%
7 6239
 
4.7%
12 5263
 
4.0%
9 4744
 
3.6%
5 4254
 
3.2%
1 3225
 
2.4%
11 2795
 
2.1%
4 2619
 
2.0%
Other values (148) 22070
 
16.7%
(Missing) 57395
43.4%
ValueCountFrequency (%)
0 747
 
0.6%
1 3225
2.4%
2 504
 
0.4%
3 765
 
0.6%
4 2619
 
2.0%
5 4254
3.2%
6 7171
5.4%
7 6239
4.7%
8 7315
5.5%
9 4744
3.6%
ValueCountFrequency (%)
965 1
< 0.1%
667 1
< 0.1%
558 1
< 0.1%
476 1
< 0.1%
471 1
< 0.1%
320 1
< 0.1%
300 1
< 0.1%
258 1
< 0.1%
215 1
< 0.1%
205 1
< 0.1%

LOW_ENERGY_FIXED_LIGHT_COUNT
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct125
Distinct (%)0.3%
Missing89802
Missing (%)67.9%
Infinite0
Infinite (%)0.0%
Mean7.0554207
Minimum0
Maximum942
Zeros6491
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2024-02-25T23:28:49.803975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q39
95-th percentile20
Maximum942
Range942
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.53721
Coefficient of variation (CV)1.6352263
Kurtosis1269.7291
Mean7.0554207
Median Absolute Deviation (MAD)4
Skewness21.204316
Sum299425
Variance133.10721
MonotonicityNot monotonic
2024-02-25T23:28:50.386209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6491
 
4.9%
1 4349
 
3.3%
4 3355
 
2.5%
6 3195
 
2.4%
3 3168
 
2.4%
5 3014
 
2.3%
2 3010
 
2.3%
10 2983
 
2.3%
8 2148
 
1.6%
7 2045
 
1.5%
Other values (115) 8681
 
6.6%
(Missing) 89802
67.9%
ValueCountFrequency (%)
0 6491
4.9%
1 4349
3.3%
2 3010
2.3%
3 3168
2.4%
4 3355
2.5%
5 3014
2.3%
6 3195
2.4%
7 2045
 
1.5%
8 2148
 
1.6%
9 1316
 
1.0%
ValueCountFrequency (%)
942 1
 
< 0.1%
577 1
 
< 0.1%
469 1
 
< 0.1%
320 1
 
< 0.1%
300 1
 
< 0.1%
258 1
 
< 0.1%
200 3
< 0.1%
180 1
 
< 0.1%
169 2
< 0.1%
166 1
 
< 0.1%

WALL_TYPE
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Solid brick
56524 
Cavity wall
47804 
Other
18719 
System built
 
5171
Timber frame
 
4019

Length

Max length12
Median length11
Mean length10.219939
Min length3

Characters and Unicode

Total characters1351495
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSolid brick
2nd rowSolid brick
3rd rowTimber frame
4th rowSolid brick
5th rowSolid brick

Common Values

ValueCountFrequency (%)
Solid brick 56524
42.7%
Cavity wall 47804
36.1%
Other 18719
 
14.2%
System built 5171
 
3.9%
Timber frame 4019
 
3.0%
Cob 4
 
< 0.1%

Length

2024-02-25T23:28:50.584183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:50.780072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
solid 56524
23.0%
brick 56524
23.0%
cavity 47804
19.5%
wall 47804
19.5%
other 18719
 
7.6%
system 5171
 
2.1%
built 5171
 
2.1%
timber 4019
 
1.6%
frame 4019
 
1.6%
cob 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 170042
12.6%
l 157303
 
11.6%
113518
 
8.4%
a 99627
 
7.4%
r 83281
 
6.2%
t 76865
 
5.7%
b 65718
 
4.9%
S 61695
 
4.6%
o 56528
 
4.2%
d 56524
 
4.2%
Other values (14) 410394
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1105736
81.8%
Uppercase Letter 132241
 
9.8%
Space Separator 113518
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 170042
15.4%
l 157303
14.2%
a 99627
9.0%
r 83281
 
7.5%
t 76865
 
7.0%
b 65718
 
5.9%
o 56528
 
5.1%
d 56524
 
5.1%
c 56524
 
5.1%
k 56524
 
5.1%
Other values (9) 226800
20.5%
Uppercase Letter
ValueCountFrequency (%)
S 61695
46.7%
C 47808
36.2%
O 18719
 
14.2%
T 4019
 
3.0%
Space Separator
ValueCountFrequency (%)
113518
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1237977
91.6%
Common 113518
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 170042
13.7%
l 157303
12.7%
a 99627
 
8.0%
r 83281
 
6.7%
t 76865
 
6.2%
b 65718
 
5.3%
S 61695
 
5.0%
o 56528
 
4.6%
d 56524
 
4.6%
c 56524
 
4.6%
Other values (13) 353870
28.6%
Common
ValueCountFrequency (%)
113518
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1351495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 170042
12.6%
l 157303
 
11.6%
113518
 
8.4%
a 99627
 
7.4%
r 83281
 
6.2%
t 76865
 
5.7%
b 65718
 
4.9%
S 61695
 
4.6%
o 56528
 
4.2%
d 56524
 
4.2%
Other values (14) 410394
30.4%

WALL_INSULATION
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
0
103025 
1
29216 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132241
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 103025
77.9%
1 29216
 
22.1%

Length

2024-02-25T23:28:50.978250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:51.131136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 103025
77.9%
1 29216
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 103025
77.9%
1 29216
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132241
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103025
77.9%
1 29216
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 132241
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103025
77.9%
1 29216
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103025
77.9%
1 29216
 
22.1%

FLOOR_TYPE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Other property below
54853 
Suspended
38539 
Solid
30433 
Other
8416 

Length

Max length20
Median length9
Mean length12.387656
Min length5

Characters and Unicode

Total characters1638156
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSolid
2nd rowSuspended
3rd rowOther property below
4th rowOther property below
5th rowSuspended

Common Values

ValueCountFrequency (%)
Other property below 54853
41.5%
Suspended 38539
29.1%
Solid 30433
23.0%
Other 8416
 
6.4%

Length

2024-02-25T23:28:51.310410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:51.484621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
other 63269
26.1%
property 54853
22.7%
below 54853
22.7%
suspended 38539
15.9%
solid 30433
12.6%

Most occurring characters

ValueCountFrequency (%)
e 250053
15.3%
r 172975
10.6%
p 148245
 
9.0%
o 140139
 
8.6%
t 118122
 
7.2%
109706
 
6.7%
d 107511
 
6.6%
l 85286
 
5.2%
S 68972
 
4.2%
O 63269
 
3.9%
Other values (8) 373878
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1396209
85.2%
Uppercase Letter 132241
 
8.1%
Space Separator 109706
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 250053
17.9%
r 172975
12.4%
p 148245
10.6%
o 140139
10.0%
t 118122
8.5%
d 107511
7.7%
l 85286
 
6.1%
h 63269
 
4.5%
b 54853
 
3.9%
w 54853
 
3.9%
Other values (5) 200903
14.4%
Uppercase Letter
ValueCountFrequency (%)
S 68972
52.2%
O 63269
47.8%
Space Separator
ValueCountFrequency (%)
109706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1528450
93.3%
Common 109706
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 250053
16.4%
r 172975
11.3%
p 148245
9.7%
o 140139
9.2%
t 118122
 
7.7%
d 107511
 
7.0%
l 85286
 
5.6%
S 68972
 
4.5%
O 63269
 
4.1%
h 63269
 
4.1%
Other values (7) 310609
20.3%
Common
ValueCountFrequency (%)
109706
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1638156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 250053
15.3%
r 172975
10.6%
p 148245
 
9.0%
o 140139
 
8.6%
t 118122
 
7.2%
109706
 
6.7%
d 107511
 
6.6%
l 85286
 
5.2%
S 68972
 
4.2%
O 63269
 
3.9%
Other values (8) 373878
22.8%

FLOOR_INSULATION
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
0
71255 
1
60986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132241
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 71255
53.9%
1 60986
46.1%

Length

2024-02-25T23:28:51.665983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:51.830405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 71255
53.9%
1 60986
46.1%

Most occurring characters

ValueCountFrequency (%)
0 71255
53.9%
1 60986
46.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132241
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71255
53.9%
1 60986
46.1%

Most occurring scripts

ValueCountFrequency (%)
Common 132241
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71255
53.9%
1 60986
46.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71255
53.9%
1 60986
46.1%

ROOF_TYPE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Pitched
63753 
Other property above
49603 
Other
10726 
Flat
8159 

Length

Max length20
Median length7
Mean length11.528928
Min length4

Characters and Unicode

Total characters1524597
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPitched
2nd rowPitched
3rd rowPitched
4th rowPitched
5th rowOther property above

Common Values

ValueCountFrequency (%)
Pitched 63753
48.2%
Other property above 49603
37.5%
Other 10726
 
8.1%
Flat 8159
 
6.2%

Length

2024-02-25T23:28:52.053528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:52.236618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
pitched 63753
27.5%
other 60329
26.1%
property 49603
21.4%
above 49603
21.4%
flat 8159
 
3.5%

Most occurring characters

ValueCountFrequency (%)
e 223288
14.6%
t 181844
11.9%
r 159535
10.5%
h 124082
 
8.1%
99206
 
6.5%
o 99206
 
6.5%
p 99206
 
6.5%
i 63753
 
4.2%
P 63753
 
4.2%
d 63753
 
4.2%
Other values (8) 346971
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1293150
84.8%
Uppercase Letter 132241
 
8.7%
Space Separator 99206
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 223288
17.3%
t 181844
14.1%
r 159535
12.3%
h 124082
9.6%
o 99206
7.7%
p 99206
7.7%
i 63753
 
4.9%
d 63753
 
4.9%
c 63753
 
4.9%
a 57762
 
4.5%
Other values (4) 156968
12.1%
Uppercase Letter
ValueCountFrequency (%)
P 63753
48.2%
O 60329
45.6%
F 8159
 
6.2%
Space Separator
ValueCountFrequency (%)
99206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1425391
93.5%
Common 99206
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 223288
15.7%
t 181844
12.8%
r 159535
11.2%
h 124082
8.7%
o 99206
 
7.0%
p 99206
 
7.0%
i 63753
 
4.5%
P 63753
 
4.5%
d 63753
 
4.5%
c 63753
 
4.5%
Other values (7) 283218
19.9%
Common
ValueCountFrequency (%)
99206
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1524597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 223288
14.6%
t 181844
11.9%
r 159535
10.5%
h 124082
 
8.1%
99206
 
6.5%
o 99206
 
6.5%
p 99206
 
6.5%
i 63753
 
4.2%
P 63753
 
4.2%
d 63753
 
4.2%
Other values (8) 346971
22.8%

ROOF_INSULATION
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
1
92704 
0
39537 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters132241
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 92704
70.1%
0 39537
29.9%

Length

2024-02-25T23:28:52.440206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:52.609643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 92704
70.1%
0 39537
29.9%

Most occurring characters

ValueCountFrequency (%)
1 92704
70.1%
0 39537
29.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132241
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 92704
70.1%
0 39537
29.9%

Most occurring scripts

ValueCountFrequency (%)
Common 132241
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 92704
70.1%
0 39537
29.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 92704
70.1%
0 39537
29.9%

MAIN_FUEL_TYPE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
mains gas
105532 
electricity
18472 
Other
 
8237

Length

Max length11
Median length9
Mean length9.0302176
Min length5

Characters and Unicode

Total characters1194165
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmains gas
2nd rowmains gas
3rd rowmains gas
4th rowmains gas
5th rowmains gas

Common Values

ValueCountFrequency (%)
mains gas 105532
79.8%
electricity 18472
 
14.0%
Other 8237
 
6.2%

Length

2024-02-25T23:28:52.806218image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-25T23:28:52.993231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
mains 105532
44.4%
gas 105532
44.4%
electricity 18472
 
7.8%
other 8237
 
3.5%

Most occurring characters

ValueCountFrequency (%)
a 211064
17.7%
s 211064
17.7%
i 142476
11.9%
m 105532
8.8%
n 105532
8.8%
105532
8.8%
g 105532
8.8%
e 45181
 
3.8%
t 45181
 
3.8%
c 36944
 
3.1%
Other values (5) 80127
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1080396
90.5%
Space Separator 105532
 
8.8%
Uppercase Letter 8237
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 211064
19.5%
s 211064
19.5%
i 142476
13.2%
m 105532
9.8%
n 105532
9.8%
g 105532
9.8%
e 45181
 
4.2%
t 45181
 
4.2%
c 36944
 
3.4%
r 26709
 
2.5%
Other values (3) 45181
 
4.2%
Space Separator
ValueCountFrequency (%)
105532
100.0%
Uppercase Letter
ValueCountFrequency (%)
O 8237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1088633
91.2%
Common 105532
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 211064
19.4%
s 211064
19.4%
i 142476
13.1%
m 105532
9.7%
n 105532
9.7%
g 105532
9.7%
e 45181
 
4.2%
t 45181
 
4.2%
c 36944
 
3.4%
r 26709
 
2.5%
Other values (4) 53418
 
4.9%
Common
ValueCountFrequency (%)
105532
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 211064
17.7%
s 211064
17.7%
i 142476
11.9%
m 105532
8.8%
n 105532
8.8%
105532
8.8%
g 105532
8.8%
e 45181
 
3.8%
t 45181
 
3.8%
c 36944
 
3.1%
Other values (5) 80127
 
6.7%

Interactions

2024-02-25T23:28:34.044500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:10.328169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:11.955961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:13.864787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:16.120736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:18.375677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:20.518410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:23.189088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:25.685275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:28.301053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:31.530206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:34.219301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:10.470466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:12.089820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:14.110444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:16.318762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:18.540634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:20.809991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:23.443854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:25.992130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:28.535393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:31.803081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:34.373037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:10.619482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:12.243811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:14.301723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:16.545349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:18.737374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:21.004926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:23.709711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:26.301567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:28.767610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:32.059825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:34.562502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:10.760874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:12.418713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:14.508668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:16.755095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:18.928084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:21.208758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:23.950785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:26.537523image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:29.032337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:32.287644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:34.749423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:10.897211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:12.602721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:14.729489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:16.952007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:19.122447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:21.438061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:24.164316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:26.777564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:29.317158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:32.497304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:34.927945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:11.030581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:12.766287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:14.938981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:17.146453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:19.291214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:21.666144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:24.385688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:27.024195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:29.670294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:32.734751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:35.124406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:11.201099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:12.919303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:15.137712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:17.342037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:19.464381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:21.934114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:24.597145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:27.311766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:30.104224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:33.004972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:35.302046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:11.353266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:13.084872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:15.338165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:17.525439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:19.637727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:22.116441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:24.791117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:27.502357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:30.336462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:33.218926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:35.507247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:11.528476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:13.274838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:15.520121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:17.736463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:19.822242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:22.374823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:25.009655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:27.701369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:30.624562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:33.422878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:39.511967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:11.677729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:13.475630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:15.722900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:18.007173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:20.057433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:22.617845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:25.228888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:27.908612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:30.917874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:33.656047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:39.656250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:11.830502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:13.668552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:15.898351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:18.169978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:20.273929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:22.885465image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:25.418201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:28.078379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:31.250399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-25T23:28:33.854303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-02-25T23:28:39.930449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-25T23:28:40.723615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CURRENT_ENERGY_RATINGPROPERTY_TYPEBUILT_FORMTOTAL_FLOOR_AREAMAINS_GAS_FLAGFLAT_TOP_STOREYFLAT_STOREY_COUNTMULTI_GLAZE_PROPORTIONEXTENSION_COUNTNUMBER_HABITABLE_ROOMSNUMBER_HEATED_ROOMSLOW_ENERGY_LIGHTINGNUMBER_OPEN_FIREPLACESWIND_TURBINE_COUNTFLOOR_HEIGHTPHOTO_SUPPLYSOLAR_WATER_HEATING_FLAGCONSTRUCTION_AGE_BANDFIXED_LIGHTING_OUTLETS_COUNTLOW_ENERGY_FIXED_LIGHT_COUNTWALL_TYPEWALL_INSULATIONFLOOR_TYPEFLOOR_INSULATIONROOF_TYPEROOF_INSULATIONMAIN_FUEL_TYPE
22596CHouseSemi-Detached171.001.0NaNNaN100.02.07.07.0100.00.00.0NaN0.0NaNEngland and Wales: 1900-192924.024.0Solid brick0Solid0Pitched0mains gas
95416DHouseEnd-Terrace56.001.0NaNNaN100.01.04.04.0100.00.00.0NaNNaN0.0England and Wales: 1900-1929NaNNaNSolid brick0Suspended0Pitched1mains gas
118768CFlatMid-Terrace24.001.01.0NaN100.00.01.01.0100.00.00.0NaNNaN0.0England and Wales: 1900-1929NaNNaNTimber frame0Other property below1Pitched1mains gas
65619CFlatSemi-Detached33.081.01.0NaN100.00.01.01.00.00.00.02.400.0NaNEngland and Wales: 2003-20068.00.0Solid brick1Other property below1Pitched0mains gas
19489EFlatSemi-Detached67.001.00.0NaN55.02.02.02.088.01.00.0NaN0.0NaNEngland and Wales: 1930-19498.07.0Solid brick0Suspended0Other property above1mains gas
152082BFlatDetached109.00NaN0.0NaN100.0NaNNaNNaN100.00.00.02.55NaNNaNEngland and Wales: 2012 onwards4.0NaNOther0Other0Other property above1Other
52692DHouseMid-Terrace118.001.0NaNNaN100.00.05.05.058.00.00.0NaN0.0NaNEngland and Wales: before 190012.07.0Solid brick0Solid0Pitched1mains gas
23920CMaisonetteEnd-Terrace48.201.01.0NaN100.00.02.02.00.00.00.02.270.0NaNEngland and Wales: 1983-199014.00.0Cavity wall0Other property below1Pitched1mains gas
19054EHouseMid-Terrace77.001.0NaNNaN0.00.04.04.00.01.00.0NaN0.0NaNEngland and Wales: before 190011.00.0Solid brick0Suspended0Pitched0mains gas
68456FHouseDetached161.001.0NaNNaN100.00.07.07.0100.00.00.0NaNNaN0.0England and Wales: 1930-1949NaNNaNCavity wall0Suspended0Pitched0mains gas
CURRENT_ENERGY_RATINGPROPERTY_TYPEBUILT_FORMTOTAL_FLOOR_AREAMAINS_GAS_FLAGFLAT_TOP_STOREYFLAT_STOREY_COUNTMULTI_GLAZE_PROPORTIONEXTENSION_COUNTNUMBER_HABITABLE_ROOMSNUMBER_HEATED_ROOMSLOW_ENERGY_LIGHTINGNUMBER_OPEN_FIREPLACESWIND_TURBINE_COUNTFLOOR_HEIGHTPHOTO_SUPPLYSOLAR_WATER_HEATING_FLAGCONSTRUCTION_AGE_BANDFIXED_LIGHTING_OUTLETS_COUNTLOW_ENERGY_FIXED_LIGHT_COUNTWALL_TYPEWALL_INSULATIONFLOOR_TYPEFLOOR_INSULATIONROOF_TYPEROOF_INSULATIONMAIN_FUEL_TYPE
39970CFlatDetached53.681.00.03.0100.00.03.03.083.00.00.02.300.00.0England and Wales: 1983-1990NaNNaNCavity wall1Other property below1Other property above1mains gas
3770BFlatNaN92.00NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaN20.020.0Other0Other property below1Other property above1mains gas
20967EFlatEnclosed End-Terrace56.221.01.0NaN100.00.03.03.096.00.00.02.560.0NaNEngland and Wales: 1930-194928.027.0Solid brick0Other property below1Flat0mains gas
154305EBungalowDetached82.001.0NaNNaN20.01.05.05.093.00.00.02.430.00.0England and Wales: 1976-198214.0NaNCavity wall1Suspended0Pitched1mains gas
1025BHouseMid-Terrace127.00NaNNaNNaNNaNNaNNaNNaN100.00.0NaNNaNNaNNaNNaN12.012.0Other0Other0Other0mains gas
161582DMaisonetteEnd-Terrace60.001.00.0NaN100.01.03.03.0100.00.00.02.510.00.0England and Wales: 1900-19298.0NaNSolid brick0Suspended0Other property above1mains gas
66889DFlatMid-Terrace39.450.00.05.00.00.02.02.00.00.00.02.290.00.0England and Wales: 1950-1966NaNNaNCavity wall0Other property below1Other property above1electricity
30764EHouseSemi-Detached142.001.0NaNNaN95.01.06.06.035.02.00.0NaNNaN0.0England and Wales: 1930-1949NaNNaNSolid brick0Suspended0Pitched0mains gas
9485CHouseMid-Terrace67.201.0NaNNaN0.00.03.03.050.00.00.02.300.00.0England and Wales: 1996-2002NaNNaNCavity wall1Solid1Pitched1mains gas
69600DFlatMid-Terrace34.001.01.0NaN100.01.02.02.050.00.00.0NaNNaN0.0England and Wales: 1950-1966NaNNaNSolid brick0Other property below1Pitched1mains gas

Duplicate rows

Most frequently occurring

CURRENT_ENERGY_RATINGPROPERTY_TYPEBUILT_FORMTOTAL_FLOOR_AREAMAINS_GAS_FLAGFLAT_TOP_STOREYFLAT_STOREY_COUNTMULTI_GLAZE_PROPORTIONEXTENSION_COUNTNUMBER_HABITABLE_ROOMSNUMBER_HEATED_ROOMSLOW_ENERGY_LIGHTINGNUMBER_OPEN_FIREPLACESWIND_TURBINE_COUNTFLOOR_HEIGHTPHOTO_SUPPLYSOLAR_WATER_HEATING_FLAGCONSTRUCTION_AGE_BANDFIXED_LIGHTING_OUTLETS_COUNTLOW_ENERGY_FIXED_LIGHT_COUNTWALL_TYPEWALL_INSULATIONFLOOR_TYPEFLOOR_INSULATIONROOF_TYPEROOF_INSULATIONMAIN_FUEL_TYPE# duplicates
227BFlatDetached51.0NaN0.0NaN100.0NaNNaNNaN100.00.00.0NaNNaNNaNEngland and Wales: 2012 onwards1.0NaNOther0Other property below1Other property above1Other58
182BFlatDetached50.0NaN0.0NaN100.0NaNNaNNaN100.00.00.0NaNNaNNaNEngland and Wales: 2012 onwards1.0NaNOther0Other property below1Other property above1Other53
4310CMaisonetteMid-Terrace81.01.00.0NaN0.00.03.03.020.00.00.0NaN0.0NaNEngland and Wales: 1967-197510.02.0Cavity wall0Other property below1Other property above1mains gas52
560BFlatDetached71.0NaN0.0NaN100.0NaNNaNNaN100.00.00.0NaNNaNNaNEngland and Wales: 2012 onwards1.0NaNOther0Other property below1Other property above1Other40
1108BFlatEnd-Terrace69.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaN9.09.0Other0Other property below1Other property above1Other38
321BFlatDetached54.0NaN0.0NaN100.0NaNNaNNaN100.00.00.0NaNNaNNaNEngland and Wales: 2012 onwards10.0NaNOther0Other property below1Other property above1Other36
424BFlatDetached63.0NaN0.0NaN100.0NaNNaNNaN100.00.00.0NaNNaNNaNEngland and Wales: 2012 onwards10.0NaNOther0Other property below1Other property above1Other34
653BFlatDetached74.0NaN0.0NaN100.0NaNNaNNaN100.00.00.0NaNNaNNaNEngland and Wales: 2012 onwards1.0NaNOther0Other property below1Other property above1Other31
1105BFlatEnd-Terrace67.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaN9.09.0Other0Other property below1Other property above1Other31
1377BFlatMid-Terrace66.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaN9.09.0Other0Other property below1Other property above1Other31